Senior Manager - AI (Architect)

Key Responsibilities

• Translate business strategies into AI roadmaps and target architectures that guide enterprise-wide adoption.
• Lead architecture governance, establishing reference patterns for RAG, fine‑tuning, classical ML, and hybrid search/graph solutions.
• Define and enforce NFRs, SLOs, and FinOps guardrails to ensure scalable, reliable, and cost‑efficient AI systems.
• Design and deliver end‑to‑end AI solutions, from data acquisition and feature engineering to model/prompt development and production deployment.
• Embed security, privacy, and compliance controls across AI workflows, including PII protection and audit readiness.
• Establish evaluation frameworks (offline metrics, HITL, A/B testing, safety filters) to ensure quality, safety, and robustness of GenAI and ML solutions.
• Integrate AI systems with enterprise data platforms, vector databases, graph stores, and ontology‑driven data contracts for interoperability.
• Implement and mature ML/LLMOps practices, including CI/CD pipelines, registries, observability, drift monitoring, and automated rollback processes.
• Drive Responsible AI and governance, partnering with legal, risk, and compliance teams to meet regulatory and ethical standards.
• Lead cross‑functional teams, mentor engineers, promote agile ways of working, and communicate complex topics to technical and non‑technical stakeholders

Essential Requirements

• Bachelor’s or Master’s degree in computer science, IT, or other quantitative disciplines.

• 10+ years overall experience, including 5+ years in AI/ML or data‑intensive solution architecture at enterprise scale.
• Demonstrated success delivering production AI systems that meet enterprise NFRs (security, latency, cost, compliance).
• Strong engineering background in at least two programming languages (e.g., Python, Java, Scala).
• Deep expertise in cloud‑native architectures (Kubernetes, microservices, event streaming) and at least one major cloud platform.
• Practical proficiency with MLOps/LLMOps tools: model/prompt registries, evaluators, feature stores, pipelines.
• Strong understanding of NLP, semantic search, and text mining techniques.

• Ability to manage multiple priorities, work under tight deadlines, and operate within a global team environment.

Desired Requirements

• Experience in the pharmaceutical industry with understanding of industry‑specific data standards.
• Domain expertise in at least one area Pharma R&D,Manufacturing, Procurement & Supply Chain, Marketing & Sales
• Strong background in semantic technologies and knowledge representation (OWL, RDF, SPARQL, SWRL, JSON‑LD, Turtle).